Data manufacturing frameworks for synthesizing synthetic training data to facilitate training a natural language to logical form model

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• accessing original training data, the original training data including a plurality of utterances, a plurality of logical forms, and database schema information, each logical form of the plurality of logical forms corresponding to at least one utterance of the plurality of utterances;
• generating a plurality of templates, each template of the plurality of templates including a delexicalized version of an utterance in the plurality of utterances and a delexicalized version of a logical form corresponding to the utterance, wherein the delexicalized version of the utterance comprises a non-terminal symbol that represents an element associated with the database schema information;
• learning a grammar from the plurality of logical forms, the grammar defining a plurality of production rules for lexicalizing the plurality of templates;
• generating synthetic training data, wherein generating the synthetic training data comprises:
• for each respective template of the plurality of templates:
• identifying one or more constraints for respective template based on the non-terminal symbol of the delexicalized version of the utterance for the respective template, sampling a database to identify a sampling component of the database based on the one or more constraints, lexicalizing the delexicalized version of the utterance for the respective template by replacing the non-terminal symbol of the delexicalized version of the utterance for the respective template with the sampling component;
• and
• training a machine learning model with the original training data and the synthetic training data to result in a trained machine learning model that is configured to translate utterances to logical forms.

A computer-program product tangibly embodied in one or more non-transitory machine-readable media, including instructions configured to cause one or more data processors to perform the following operations:

• accessing original training data, the original training data including a plurality of utterances, a plurality of logical forms, and database schema information, each logical form of the plurality of logical forms corresponding to at least one utterance of the plurality of utterances;
• generating a plurality of templates, each template of the plurality of templates including a delexicalized version of an utterance in the plurality of utterances and a delexicalized version of a logical form corresponding to the utterance, wherein the delexicalized version of the utterance comprises a non-terminal symbol that represents an element associated with the database schema information;
• learning a grammar from the plurality of logical forms, the grammar defining a plurality of production rules for lexicalizing the plurality of templates;
• generating synthetic training data, wherein generating the synthetic training data comprises:
• for each respective template of the plurality of templates:
• identifying one or more constraints for respective template based on the non-terminal symbol of the delexicalized version of the utterance for the respective template, sampling a database to identify a sampling component of the database based on the one or more constraints, lexicalizing the delexicalized version of the utterance for the respective template by replacing the non-terminal symbol of the delexicalized version of the utterance for the respective template with the sampling component;
• and
• training a machine learning model with the original training data and the synthetic training data to result in a trained machine learning model that is configured to translate utterances to logical forms.